Data visualization: When it's the wrong tool for the job

A picture's worth a thousand words -- as long as it's the right image for the right audience. Learn best practices for when to use a data visualization and who to invite to those meetings.

bigdata082613.gif

It's a challenge to explain data; it's an even bigger challenge to explain big data. There are many nuances about data that are hard to efficiently communicate. Data visualization is one way leaders are meeting this challenge, and it's an important capability to incorporate into your data science team -- just make sure you have the right objective and audience before deciding on this approach.

The purpose of a data visualization

A good data visualization presents raw data in a graphical format. Contrast this with an infographic, which includes some degree of analysis and inference. A data visualization is neutral; whereas an infographic tells a story that supports a opinion or point of view. A data visualization supports discovery; whereas an infographic supports influence.

A cartographic map is a terrific data visualization. According to Edward Tufte, noted expert on visualization, a map is a brilliant example of how to graphically display geographic information. There is no agenda to a map -- it simply displays raw, geographic data in a visual format. If a city appears bigger than others on a map, it's because the city has a larger population, not because it's a great spot for the next company all-hands meeting.

There is a lot of data in a map that can answer a wide array of questions, depending on the specific question the reader is asking. This is the attitude your experts should have when creating your data visualizations, and it should inform your decisions for whom to invite to a data visualization meeting.

Data visualization consumers

Facilitations with data visualizations are a bit unusual; however, they work well as long as you have the right audience. The people who receive the most value from a data visualization are the people who are willing to invest time to discover your data.

A mistake I commonly see with data science leaders is trying to make a quick point to an executive with a data visualization. Most executives don't have time for data visualizations; you should never bring a data visualization into a meeting with a top executive to summarize your findings -- this is the wrong tool for the job. Instead, save your data visualizations for people who already have a vested interest in what you're trying to accomplish and are willing to spend time staring at the data. Examples might be key stakeholders in analytic functions (e.g., finance analysts), executives who already have an analytic slant such as the CIO, or other data scientists.

The objective of the meeting should be to discover insights from the data you've collected. For instance, you may have a network diagram that shows how your customers are connected to each other. And even though there may be some obvious clusters that stand out, your job is to just display the data and have the audience discover this on their own. After explaining the objective to your meeting participants, you should have a good block of time (say 10 minutes), where everyone just stares at the data visualization; this gives them the opportunity to discover their own insights. After that you might share your insights and discuss others' insights and possibly questions that need to be further explored.

Summary

Data visualizations are great tools that you and your data science team can use to explain their findings. When using visualizations in a meeting, make sure your participants have the time, motivation, and inclination to process the data on their own. Whatever you do, don't bring a data visualization into a meeting with top executives who have very limited time, or the only thing they'll be visualizing is where they'd rather be.

John, I'm struggling to understand this: " After explaining the objective to your meeting participants, you should have a good block of time (say 10 minutes), where everyone just stares at the data visualization." In my experience, insight from data visualization comes from interacting with, and manipulation of, data. That's where hidden patterns and new insights come from. I'm not sure what value people get from just staring at a data visualization?